Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning
The paper presents a novel approach to Chain-of-Thought (CoT) graph learning by interpreting it through the lens of clustering. It introduces a unified framework called KCoT, which integrates CoT reasoning with graph representation learning. Experimental results indicate that this method consistently outperforms existing state-of-the-art techniques.
- ▪The authors propose a $k$-means interpretation of iterative reasoning in graph-structured data.
- ▪KCoT integrates CoT reasoning with graph representation learning to enhance interpretability.
- ▪Experiments show that the proposed method achieves consistent improvements over current methods.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24867 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.24867 (cs) [Submitted on 24 May 2026] Title:Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning Authors:Xuanting Xie, Zhaochen Guo, Bingheng Li, Xingtong Yu, Zhifei Liao, Zhao Kang, Yuan Fang View a PDF of the paper titled Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning, by Xuanting Xie and 6 other authors View PDF HTML (experimental) Abstract:Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs).
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